Efficient compiler code generation for Deep Learning Snowflake co-processor

Deep Neural Networks (DNNs) are widely used in various applications including image classification, semantic segmentation and natural language processing. Various DNN models were developed to achieve high accuracy on different tasks. Efficiently mapping the workflow of those models onto custom accelerators requires a programmable hardware and a custom compiler. In this work, we use Snowflake, which is a programmable DNN targeted accelerator. We also present a compiler that correctly generated code for Snowflake. Our system were evaluated on various convolution layers present in AlexNet, ResNet and LightCNN. Snowflake with 256 processing units was implemented on Xilinx FPGA, and it achieved 70 frames/s for AlexNet without linear layers.